Being transparent about the use of data collected during clinical care is important to establish trust relationships between patients and researchers. We propose to develop a system to elicit patient preferences for clinical data sharing that takes into account what data are going to be shared and who is going to be the recipient of shared data. Lessons learned from a pilot study indicate that providing such options in a real clinical setting does not result in massive patient withdrawal in data sharing. The proposed project will generate practical tools and knowledge to guide the development and implementation of informed consent management systems. We plan to conduct a large-scale study in which we will: (1) Determine the best way to present data sharing preferences to patients. Specifically, we will compare preferences elicited via a simple interface (where data categories, such as laboratory tests, and data recipients, such as researchers working in non-profit institutions, will be available) or a complex interface (where items within each data category and within each category of recipients will be available, such as genetic tests and researchers working in the pharmaceutical industry, respectively). These selections will be honored by the research data delivery team through links to our clinical data warehouse for research. (2) Study patient characteristics associated with data sharing preferences for 1,200 randomly sampled patients from 39 diverse general and specialty clinics. Where applicable, we will also compare patient selections for their own data to selections they would make as surrogate decision makers for others. We will conduct surveys where patients can indicate their subjective perception of disease, and we will objectively assess disease severity from EHR data for comparison. This will help us understand whether disease severity plays a role in data sharing preferences. (3) Statistically analyze the degree to which patient preferences affect shared data. This will be important so we can ascertain the quality of data that are shared for research.